# Computations
import numpy as np
import pandas as pd

# scipy
from scipy.stats import norm

# preprocessing
from sklearn import preprocessing
import re

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

## WordCloud
from wordcloud import WordCloud


import warnings
warnings.filterwarnings("ignore")

IBM HR Analytics Employee Attrition and Performance Dataset

In this study, we analyze HR data available from kaggle.com

This data is fictional and it is created by IBM data scientists.

Categorical Parameters:

	1	2	3	4	5
Education	Below College	College	Bachelor	Master	Doctor
Environment Satisfaction	Low	Medium	High	Very High
Job Involvement	Low	Medium	High	Very High
Job Satisfaction	Low	Medium	High	Very High
Performance Rating	Low	Good	Excellent	Outstanding
Relationship Satisfaction	Low	Medium	High	Very High
WorkLife Balance	Bad	Good	Better	Best

This can be encoded as follows,

Categorical_Dict = {'Education': {1:'Below College', 2:'College',3:'Bachelor', 4: 'Master', 5:'Doctor'},
                    'Environment Satisfaction': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Job Involvement': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Job Satisfaction': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Performance Rating': {1:'Low', 2:'Good', 3:'Excellent', 4:'Outstanding'},
                    'Relationship Satisfaction': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Work Life Balance': {1:'Bad', 2:'Good', 3:'Better', 4:'Best'}}

Loading the Dataset

Data = pd.read_excel('Data/WA_Fn-UseC_-HR-Employee-Attrition.xlsx')
Temp = [re.sub(r"(\w)([A-Z])", r"\1 \2", x) for x in Data.columns]
Temp = [x.replace(' Curr ', ' Current ').replace('18',' 18').replace('Num ','Number Of ') for x in Temp]
Data.columns = Temp
del Temp
Data['Business Travel'] = Data['Business Travel'].str.replace('_',' ')
display(Data.head(8).style.hide_index())
Target = 'Attrition'

Age	Attrition	Business Travel	Daily Rate	Department	Distance From Home	Education	Education Field	Employee Count	Employee Number	Environment Satisfaction	Gender	Hourly Rate	Job Involvement	Job Level	Job Role	Job Satisfaction	Marital Status	Monthly Income	Monthly Rate	Number Of Companies Worked	Over 18	Over Time	Percent Salary Hike	Performance Rating	Relationship Satisfaction	Standard Hours	Stock Option Level	Total Working Years	Training Times Last Year	Work Life Balance	Years At Company	Years In Current Role	Years Since Last Promotion	Years With Current Manager
41	Yes	Travel Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
49	No	Travel Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
37	Yes	Travel Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
33	No	Travel Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
27	No	Travel Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2
32	No	Travel Frequently	1005	Research & Development	2	2	Life Sciences	1	8	4	Male	79	3	1	Laboratory Technician	4	Single	3068	11864	0	Y	No	13	3	3	80	0	8	2	2	7	7	3	6
59	No	Travel Rarely	1324	Research & Development	3	3	Medical	1	10	3	Female	81	4	1	Laboratory Technician	1	Married	2670	9964	4	Y	Yes	20	4	1	80	3	12	3	2	1	0	0	0
30	No	Travel Rarely	1358	Research & Development	24	1	Life Sciences	1	11	4	Male	67	3	1	Laboratory Technician	3	Divorced	2693	13335	1	Y	No	22	4	2	80	1	1	2	3	1	0	0	0

First off, let's take a look at the dataset

def Data_Plot(Inp, W = False):
    data_info = Inp.dtypes.astype(str).to_frame(name='Data Type')
    Temp = Inp.isnull().sum().to_frame(name = 'Number of NaN Values')
    data_info = data_info.join(Temp, how='outer')
    data_info ['Size'] = Inp.shape[0]
    data_info['Percentage'] = 100 - np.round(100*(data_info['Number of NaN Values']/Inp.shape[0]),2)
    data_info.index.name = 'Features'
    data_info = data_info.reset_index(drop = False)
    #
    fig = px.bar(data_info, x= 'Features', y= 'Percentage', color = 'Data Type', text = 'Data Type',
                color_discrete_sequence = ['PaleGreen', 'LightCyan', 'PeachPuff', 'Pink', 'Plum'],
                 hover_data = data_info.columns)
    fig.update_layout(plot_bgcolor= 'white', legend=dict(x=1.01, y=.5, traceorder="normal",
                                                         bordercolor="DarkGray", borderwidth=1))
    fig.update_traces(texttemplate= 6*' ' + '%{label}', textposition='inside')
    fig.update_traces(marker_line_color= 'Black', marker_line_width=1., opacity=1)
    if W:
        fig.update_layout(width = W)
    fig.show()
    return data_info
    
    
_ = Data_Plot(Data)

A quick overview of data distribution:

_ = Data.hist(bins=30, grid=False, figsize=(18,18), color='#34495e', edgecolor='k', zorder=2, rwidth=0.8)

Exploratory Data Analysis

## Attrition Colormap
Att_Colors = ['LightSalmon', 'LightBlue']
Att_LC = 'Black'
# Gender Colormap
MF_Colors = ['HotPink', 'RoyalBlue']
MF_LC = 'Navy'
# Education
Ed_Colors = ['LightCoral','Khaki','GreenYellow','LimeGreen','ForestGreen']
Ed_LC = 'Black'

def DistPlot(Feat, yLim = [0, 80], H = 450, titleY = 0.92):
    fig = px.histogram(Data, x = Feat, color='Attrition', marginal= 'box',
                       color_discrete_sequence= Att_Colors, hover_data=Data.columns)
    fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=False, gridwidth=1, gridcolor='Lightgray')
    fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=True, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=True, gridwidth=1, gridcolor='Lightgray')
    Name = '%s Distribution by Attrition' % Feat
    fig.update_layout(legend_orientation='v', plot_bgcolor= 'white', height= H, width= 980,
                     title={'text': '<b>' + Name + '<b>', 'x':0.5, 'y': titleY, 'xanchor': 'center', 'yanchor': 'top'},
                     yaxis_title='Frequency')
    fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
    fig['layout']['yaxis'].update(range=yLim)
    fig.show()
    
def PlotX1(df, Feat, ColorFeat = 'Gender', yLim = [0, 35], H = 500, titleY = 0.90):
    fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.02, shared_yaxes=True,
#                         x_title = Feat,
                        y_title = 'Percent',
                        subplot_titles=('Attrition: No', 'Attrition: Yes'))
    # Left
    fig1 = px.bar(df.loc[df.Attrition == 'No'], x=  Feat, y= 'Percentage', orientation='v',
                  color = ColorFeat, text = 'Percentage', hover_data= df.columns,
                  color_discrete_sequence = MF_Colors)

    for i in range(len(fig1['data'])):
        fig.add_trace(fig1['data'][i], row=1, col=1)
    fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

    # Right
    fig2 = px.bar(df.loc[df.Attrition == 'Yes'], x= Feat, y= 'Percentage', orientation='v',
                  color = ColorFeat, text = 'Percentage', hover_data= df.columns,
                  color_discrete_sequence = MF_Colors)

    for i in range(len(fig2['data'])):
        fig.add_trace(fig2['data'][i], row=1, col=2)
    fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=1, col=2)

    # Update
    fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=False, gridwidth=1, gridcolor='Lightgray')
    fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=True, gridwidth=1, gridcolor='Lightgray', range= yLim)
    fig.update_layout(legend_orientation='v', legend_title_text=ColorFeat, plot_bgcolor= 'white', height= H, width= 980)
    fig.update_layout(legend=dict(font=dict(color="Black"), bordercolor="Lightgray", borderwidth=1))
    Name = '%s Distribution by %s and Attrition' % (Feat, ColorFeat)
    fig.update_layout(title={'text': '<b>' + Name + '<b>', 'x':0.5, 'y': titleY, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show() 
    
def PlotX2(df, Feat, ColorFeat = 'Education', yLim = [0, 35], H = 500, titleY = 0.90):
    fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.02, shared_yaxes=True,
#                         x_title = Feat,
                        y_title = 'Percent',
                        subplot_titles=('Attrition: No', 'Attrition: Yes'))
    # Left
    fig1 = px.bar(df.loc[df.Attrition == 'No'], x= Feat, y= 'Percentage', orientation='v', barmode='group',
                  color = ColorFeat, text = 'Percentage', hover_data= df.columns, 
                  color_discrete_sequence = Ed_Colors)
    for i in range(len(fig1['data'])):
        fig.add_trace(fig1['data'][i], row=1, col=1)
    fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

    # Right
    fig2 = px.bar(df.loc[df.Attrition == 'Yes'], x= Feat, y= 'Percentage', orientation='v',
                  color = ColorFeat, text = 'Percentage', hover_data= df.columns,
                  color_discrete_sequence = Ed_Colors)

    for i in range(len(fig2['data'])):
        fig.add_trace(fig2['data'][i], row=1, col=2)
    fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=1, col=2)

    # Update
    fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=False, gridwidth=1, gridcolor='Lightgray')
    fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=True, gridwidth=1, gridcolor='Lightgray', range= yLim)
    fig.update_layout(legend_orientation='v', legend_title_text=ColorFeat, plot_bgcolor= 'white', height= H, width= 980)
    fig.update_layout(legend=dict(font=dict(color="Black"), bordercolor="Lightgray", borderwidth=1))
    Name = '%s Distribution by %s and Attrition' % (Feat, ColorFeat)
    fig.update_layout(title={'text': '<b>' + Name + '<b>', 'x':0.5, 'y': titleY, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()   

def PlotY1(df, Feat, ColorFeat = 'Gender', xLim = [0, 35], H = 500, titleY = 0.90):
    fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_yaxes=True,
#                         y_title = Feat,
                        x_title = 'Percent',
                        subplot_titles=('Attrition: No', 'Attrition: Yes'))
    # Top
    fig1 = px.bar(df.loc[df.Attrition == 'No'], y=  Feat, x= 'Percentage', orientation='h',
                  color = ColorFeat, text = 'Percentage', hover_data= df.columns,
                  color_discrete_sequence = MF_Colors)

    for i in range(len(fig1['data'])):
        fig.add_trace(fig1['data'][i], row=1, col=1)
    fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

    # Bottom
    fig2 = px.bar(df.loc[df.Attrition == 'Yes'], y= Feat, x= 'Percentage', orientation='h',
                  color = ColorFeat, text = 'Percentage', hover_data= df.columns,
                  color_discrete_sequence = MF_Colors)

    for i in range(len(fig2['data'])):
        fig.add_trace(fig2['data'][i], row=2, col=1)
    fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

    # Update
    fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=True, gridwidth=1, gridcolor='Lightgray', range= xLim)
    fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=False, gridwidth=1, gridcolor='Lightgray')
    fig.update_layout(legend_orientation='v', legend_title_text=ColorFeat, plot_bgcolor= 'white', height= H, width= 980)
    fig.update_layout(legend=dict(font=dict(color="Black"), bordercolor="Lightgray", borderwidth=1))
    Name = '%s Distribution by %s and Attrition' % (Feat, ColorFeat)
    fig.update_layout(title={'text': '<b>' + Name + '<b>', 'x':0.5, 'y': titleY, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show() 
    
def PlotY2(df, Feat, ColorFeat = 'Education', xLim = [0, 35], H = 500, titleY = 0.90):
    fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_yaxes=True,
#                         y_title = Feat,
                        x_title = 'Percent',
                        subplot_titles=('Attrition: No', 'Attrition: Yes'))
    # Top
    fig1 = px.bar(df.loc[df.Attrition == 'No'], y= Feat, x= 'Percentage', orientation='h', barmode='group',
                  color = ColorFeat, text = 'Percentage', hover_data= df.columns, 
                  color_discrete_sequence = Ed_Colors)
    for i in range(len(fig1['data'])):
        fig.add_trace(fig1['data'][i], row=1, col=1)
    fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

    # Bottom
    fig2 = px.bar(df.loc[df.Attrition == 'Yes'], y= Feat, x= 'Percentage', orientation='h',
                  color = ColorFeat, text = 'Percentage', hover_data= df.columns,
                  color_discrete_sequence = Ed_Colors)

    for i in range(len(fig2['data'])):
        fig.add_trace(fig2['data'][i], row=2, col=1)
    fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

    # Update
    fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=True, gridwidth=1, gridcolor='Lightgray', range= xLim)
    fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=False, gridwidth=1, gridcolor='Lightgray')
    fig.update_layout(legend_orientation='v', legend_title_text=ColorFeat, plot_bgcolor= 'white', height= H, width= 980)
    fig.update_layout(legend=dict(font=dict(color="Black"), bordercolor="Lightgray", borderwidth=1))
    Name = '%s Distribution by %s and Attrition' % (Feat, ColorFeat)
    fig.update_layout(title={'text': '<b>' + Name + '<b>', 'x':0.5, 'y': titleY, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()   

Age

DistPlot(Feat = 'Age')
#
bins = pd.IntervalIndex.from_tuples([(0, 25), (25, 40), (40, 45),(45, 60)])
Temp = Data[['Gender','Age','Attrition']]
Temp['Age'] = pd.cut(Temp['Age'], bins)
Temp = Temp.groupby(['Gender','Age','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Age'])
Temp['Age'] = Temp['Age'].astype(str)

PlotX1(df = Temp, Feat = 'Age')
del Temp
#
bins = pd.IntervalIndex.from_tuples([(0, 25), (25, 40), (40, 45),(45, 60)])
Temp = Data[['Education','Age','Attrition']]
Temp['Age'] = pd.cut(Temp['Age'], bins)
Temp = Temp.groupby(['Education','Age','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code','Age'], inplace = True)
Temp['Age'] = Temp['Age'].astype(str)

PlotX2(df = Temp, Feat ='Age', yLim = [0, 20])
del Temp

Business Travel

Temp = Data.groupby(['Gender','Business Travel','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Business Travel'])

PlotX1(df = Temp, Feat = 'Business Travel', yLim = [0, 40], H = 500, titleY = 0.90)
del Temp

Temp = Data.groupby(['Business Travel','Education','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Business Travel'])
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
PlotX2(df = Temp, Feat = 'Business Travel', yLim = [0, 25], H = 500, titleY = 0.90)
del Temp

Department

Feat = 'Department'
Temp = Data.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])

PlotY1(df = Temp, Feat = Feat, xLim = [0, 40], H = 550, titleY = 0.90)
del Temp

Temp = Data.groupby([Feat,'Education','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
PlotX2(df = Temp, Feat = Feat, yLim = [0, 25])
del Temp, Feat

Distance from Home

Feat = 'Distance From Home'
DistPlot(Feat, yLim = [0, 250], H = 450, titleY = 0.92)


bins = pd.IntervalIndex.from_tuples([(0, 5), (5, 10), (10, 20),(20, 30)])
Temp = Data[['Gender','Distance From Home','Attrition']]
Temp['Distance From Home'] = pd.cut(Temp['Distance From Home'], bins)
Temp = Temp.groupby(['Gender','Distance From Home','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Distance From Home'])
Temp['Distance From Home'] = Temp['Distance From Home'].astype(str)

PlotX1(df = Temp, Feat = Feat, yLim = [0, 25])
del Temp

bins = pd.IntervalIndex.from_tuples([(0, 5), (5, 10), (10, 20),(20, 30)])
Temp = Data[['Education','Distance From Home','Attrition']]
Temp['Distance From Home'] = pd.cut(Temp['Distance From Home'], bins)
Temp = Temp.groupby(['Education','Distance From Home','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Distance From Home'])
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp['Distance From Home'] = Temp['Distance From Home'].astype(str)

PlotX2(df = Temp, Feat = Feat, yLim = [0, 14])
del Temp, Feat

Education

Temp = Data[['Gender','Education','Attrition']]
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp.groupby(['Gender','Education','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Education'])

PlotX1(df = Temp, Feat = 'Education', yLim = [0, 25])
del Temp

Education Field

Feat = 'Education Field'
Temp = Data.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 25])
del Temp

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
Temp[Feat] = Temp[Feat].map(lambda x: x.replace('&','and').replace(' ','\n'))

PlotX2(df = Temp, Feat = Feat, yLim = [0, 14])
del Temp, Feat

Environment Satisfaction

Feat = 'Environment Satisfaction'
Temp = Data[['Gender',Feat,'Attrition']]
Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])

PlotX1(df = Temp, Feat = Feat, yLim = [0, 20])

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 12])

Hourly Rate

Feat = 'Hourly Rate'
DistPlot(Feat, yLim = [0, 140], H = 500, titleY = 0.92)

bins = pd.IntervalIndex.from_tuples([(25, 50), (50, 75), (75, 100)])
Temp = Data[['Gender',Feat,'Attrition']]
Temp[Feat] = pd.cut(Temp[Feat], bins)
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
Temp[Feat] = Temp[Feat].astype(str)

PlotX1(df = Temp, Feat = Feat, yLim = [0, 20], H = 500, titleY = 0.90)

bins = pd.IntervalIndex.from_tuples([(25, 50), (50, 75), (75, 100)])
Temp = Data[['Education',Feat,'Attrition']]
Temp[Feat] = pd.cut(Temp[Feat], bins)
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
Temp[Feat] = Temp[Feat].astype(str)

PlotX2(df = Temp, Feat = Feat, yLim = [0, 14], H = 500, titleY = 0.90)
del Temp, Feat

Job Involvement

Feat = 'Job Involvement'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 35], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 20], H = 500, titleY = 0.90)
del Temp, Feat

Job Level

Feat = 'Job Level'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 20], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 12], H = 500, titleY = 0.90)
del Temp, Feat

Job Roles

Feat = 'Job Role'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 12], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 8], H = 500, titleY = 0.90)
del Temp, Feat

Job Satisfaction

Feat = 'Job Satisfaction'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 20], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 10], H = 500, titleY = 0.90)
del Temp, Feat

Marital Status

Feat = 'Marital Status'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 25], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 16], H = 500, titleY = 0.90)
del Temp, Feat

Number of Companies Worked

Feat = 'Number Of Companies Worked'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 20], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 12], H = 500, titleY = 0.90)
del Temp, Feat

Over Time

Feat = 'Over Time'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 40], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 30], H = 500, titleY = 0.90)
del Temp, Feat

Percent Salary Hike

Feat = 'Percent Salary Hike'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 10], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 6], H = 500, titleY = 0.90)
del Temp, Feat

Performance Rating

Feat = 'Performance Rating'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 50], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 30], H = 500, titleY = 0.90)
del Temp, Feat

Relationship Satisfaction

Feat = 'Relationship Satisfaction'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 20], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 12], H = 500, titleY = 0.90)
del Temp, Feat

Stock Option Level

Feat = 'Stock Option Level'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 25], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 16], H = 500, titleY = 0.90)
del Temp, Feat

Total Working Years

Feat = 'Total Working Years'
DistPlot(Feat = Feat, yLim = [0, 250], H = 450, titleY = 0.92)

bins = pd.IntervalIndex.from_tuples([(-1e-10, 10), (10, 20), (20, 30), (30, 40)])
Temp = Data[['Gender','Total Working Years','Attrition']]
Temp['Total Working Years'] = pd.cut(Temp['Total Working Years'], bins)
Temp = Temp.groupby(['Gender','Total Working Years','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Total Working Years'])
Temp['Total Working Years'] = Temp['Total Working Years'].astype(str)

PlotX1(df = Temp, Feat = Feat, yLim = [0, 35], H = 500, titleY = 0.90)

bins = pd.IntervalIndex.from_tuples([(-1e-10, 10), (10, 20), (20, 30), (30, 40)])
Temp = Data[['Education','Total Working Years','Attrition']]
Temp['Total Working Years'] = pd.cut(Temp['Total Working Years'], bins)
Temp = Temp.groupby(['Education','Total Working Years','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Total Working Years'])
Temp['Total Working Years'] = Temp['Total Working Years'].astype(str)
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
    
PlotX2(df = Temp, Feat = Feat, yLim = [0, 20], H = 500, titleY = 0.90)

Training Times Last Year

DistPlot(Feat = 'Training Times Last Year', yLim = [0, 600], H = 450, titleY = 0.92)

Work-Life Balance Score

Feat = 'Work Life Balance'
Temp = Data[['Gender',Feat,'Attrition']]
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])
Temp = Temp.groupby(['Gender',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=[Feat])
PlotX1(df = Temp, Feat = Feat, yLim = [0, 35], H = 500, titleY = 0.90)

Temp = Data[['Education',Feat,'Attrition']]
Temp = Temp.groupby(['Education',Feat,'Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code',Feat], inplace = True)
if Feat in Categorical_Dict.keys():
    Temp[Feat] = Temp[Feat].replace(Categorical_Dict[Feat])

PlotX2(df = Temp, Feat = Feat, yLim = [0, 20], H = 500, titleY = 0.90)
del Temp, Feat

Years at the Company

DistPlot(Feat = 'Years At Company', yLim = [0, 200], H = 450, titleY = 0.92)

Years In Current Role

DistPlot(Feat = 'Years In Current Role', yLim = [0, 400], H = 450, titleY = 0.92)

Years Since Last Promotion

DistPlot(Feat = 'Years Since Last Promotion', yLim = [0, 600], H = 450, titleY = 0.92)

Years With Current Manager

DistPlot(Feat = 'Years With Current Manager', yLim = [0, 400], H = 450, titleY = 0.92)

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References

1. Kaggle Dataset: IBM HR Analytics Employee Attrition & Performance
2. Getting Started with Plotly in Python

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